{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmarking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Prepare working environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import timm\n",
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import Dataset\n",
    "from PIL import Image\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "from scripts import Worker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Jun  4 00:39:41 2023       \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 530.51.01              Driver Version: 532.03       CUDA Version: 12.1     |\n",
      "|-----------------------------------------+----------------------+----------------------+\n",
      "| GPU  Name                  Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf            Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                      |               MIG M. |\n",
      "|=========================================+======================+======================|\n",
      "|   0  NVIDIA GeForce RTX 3090         On | 00000000:09:00.0  On |                  N/A |\n",
      "|  0%   44C    P8               30W / 350W|    809MiB / 24576MiB |      7%      Default |\n",
      "|                                         |                      |                  N/A |\n",
      "+-----------------------------------------+----------------------+----------------------+\n",
      "                                                                                         \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                            |\n",
      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
      "|        ID   ID                                                             Usage      |\n",
      "|=======================================================================================|\n",
      "|    0   N/A  N/A        22      G   /Xwayland                                 N/A      |\n",
      "|    0   N/A  N/A        31      G   /Xwayland                                 N/A      |\n",
      "+---------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'torch' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m device \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mdevice(\u001b[39m\"\u001b[39m\u001b[39mcuda:0\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39mcuda\u001b[39m.\u001b[39mis_available() \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mcpu\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      2\u001b[0m \u001b[39mprint\u001b[39m(device)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Datasets and visulisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyDataset(Dataset):\n",
    "    def __init__(self, data_dir, transforms=None):\n",
    "        self.data_dir = data_dir\n",
    "        self.data_info = self.get_img_info(data_dir)\n",
    "        self.transforms = transforms\n",
    "\n",
    "    def __getitem__(self, item):\n",
    "        path_img, label = self.data_info.iloc[item][1:3]\n",
    "        label = int(label)\n",
    "        path_img = os.path.join(self.data_dir, path_img)\n",
    "        image = Image.open(path_img).convert('RGB') # Gray scale is enough for logic interpretation\n",
    "        # 使用定义好的transforms，对数据进行处理\n",
    "        if self.transforms is not None:\n",
    "            image = self.transforms(image)\n",
    "\n",
    "        return image, label\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_info)\n",
    "    \n",
    "    def get_img_info(self, data_dir):\n",
    "        path_dir = os.path.join(data_dir, 'label.csv')\n",
    "        return pd.read_csv(path_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timm.list_models('*GoogLeNet*', pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) \n",
    "\n",
    "classes = (0, 1)\n",
    "# model_names = ['xception','densenet201','resnetv2_50', 'vgg16', 'darknet53', 'mobilenetv3_large_100', 'inception_resnet_v2', 'nasnetalarge', 'efficientnetv2_rw_m', 'convnext_xlarge_384_in22ft1k']\n",
    "model_names = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log: xception\n",
      "state dict found: ./models/dataset02_benchmarking/xception.pt\n",
      "Log: densenet201\n",
      "state dict found: ./models/dataset02_benchmarking/densenet201.pt\n",
      "Log: resnetv2_50\n",
      "state dict found: ./models/dataset02_benchmarking/resnetv2_50.pt\n",
      "Log: vgg16\n",
      "state dict found: ./models/dataset02_benchmarking/vgg16.pt\n",
      "Log: darknet53\n",
      "state dict found: ./models/dataset02_benchmarking/darknet53.pt\n",
      "Log: mobilenetv3_large_100\n",
      "state dict found: ./models/dataset02_benchmarking/mobilenetv3_large_100.pt\n",
      "Log: inception_resnet_v2\n",
      "state dict found: ./models/dataset02_benchmarking/inception_resnet_v2.pt\n",
      "Log: nasnetalarge\n",
      "state dict found: ./models/dataset02_benchmarking/nasnetalarge.pt\n",
      "Log: efficientnetv2_rw_m\n",
      "state dict found: ./models/dataset02_benchmarking/efficientnetv2_rw_m.pt\n",
      "Log: convnext_xlarge_384_in22ft1k\n",
      "state dict found: ./models/dataset02_benchmarking/convnext_xlarge_384_in22ft1k.pt\n",
      "Log: xception\n",
      "state dict found: ./models/dataset03_benchmarking/xception.pt\n",
      "Log: densenet201\n",
      "state dict found: ./models/dataset03_benchmarking/densenet201.pt\n",
      "Log: resnetv2_50\n",
      "state dict found: ./models/dataset03_benchmarking/resnetv2_50.pt\n",
      "Log: vgg16\n",
      "state dict found: ./models/dataset03_benchmarking/vgg16.pt\n",
      "Log: darknet53\n",
      "state dict found: ./models/dataset03_benchmarking/darknet53.pt\n",
      "Log: mobilenetv3_large_100\n"
     ]
    },
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       "      <th>epoch</th>\n",
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       "      <th>valid_loss</th>\n",
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       "  <thead>\n",
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       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
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     "text": [
      "Log: inception_resnet_v2\n",
      "state dict found: ./models/dataset03_benchmarking/inception_resnet_v2.pt\n",
      "Log: nasnetalarge\n",
      "state dict found: ./models/dataset03_benchmarking/nasnetalarge.pt\n",
      "Log: efficientnetv2_rw_m\n",
      "state dict found: ./models/dataset03_benchmarking/efficientnetv2_rw_m.pt\n",
      "Log: convnext_xlarge_384_in22ft1k\n",
      "state dict found: ./models/dataset03_benchmarking/convnext_xlarge_384_in22ft1k.pt\n",
      "Log: xception\n",
      "state dict found: ./models/dataset04_benchmarking/xception.pt\n",
      "Log: densenet201\n",
      "state dict found: ./models/dataset04_benchmarking/densenet201.pt\n",
      "Log: resnetv2_50\n"
     ]
    },
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     "text": [
      "Log: vgg16\n",
      "state dict found: ./models/dataset04_benchmarking/vgg16.pt\n",
      "Log: darknet53\n",
      "state dict found: ./models/dataset04_benchmarking/darknet53.pt\n",
      "Log: mobilenetv3_large_100\n",
      "state dict found: ./models/dataset04_benchmarking/mobilenetv3_large_100.pt\n",
      "Log: inception_resnet_v2\n",
      "state dict found: ./models/dataset04_benchmarking/inception_resnet_v2.pt\n",
      "Log: nasnetalarge\n",
      "state dict found: ./models/dataset04_benchmarking/nasnetalarge.pt\n",
      "Log: efficientnetv2_rw_m\n",
      "state dict found: ./models/dataset04_benchmarking/efficientnetv2_rw_m.pt\n",
      "Log: convnext_xlarge_384_in22ft1k\n",
      "state dict found: ./models/dataset04_benchmarking/convnext_xlarge_384_in22ft1k.pt\n",
      "Log: xception\n",
      "state dict found: ./models/dataset05_benchmarking/xception.pt\n",
      "Log: densenet201\n",
      "state dict found: ./models/dataset05_benchmarking/densenet201.pt\n",
      "Log: resnetv2_50\n"
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      "Log: vgg16\n",
      "state dict found: ./models/dataset05_benchmarking/vgg16.pt\n",
      "Log: darknet53\n",
      "state dict found: ./models/dataset05_benchmarking/darknet53.pt\n",
      "Log: mobilenetv3_large_100\n",
      "state dict found: ./models/dataset05_benchmarking/mobilenetv3_large_100.pt\n",
      "Log: inception_resnet_v2\n",
      "state dict found: ./models/dataset05_benchmarking/inception_resnet_v2.pt\n",
      "Log: nasnetalarge\n",
      "state dict found: ./models/dataset05_benchmarking/nasnetalarge.pt\n",
      "Log: efficientnetv2_rw_m\n",
      "state dict found: ./models/dataset05_benchmarking/efficientnetv2_rw_m.pt\n",
      "Log: convnext_xlarge_384_in22ft1k\n",
      "state dict found: ./models/dataset05_benchmarking/convnext_xlarge_384_in22ft1k.pt\n"
     ]
    }
   ],
   "source": [
    "for dataset_folder in [\"dataset02\", \"dataset03\", \"dataset04\", \"dataset05\"]:\n",
    "    trainset = MyDataset(f\"data/train/{dataset_folder}\", transform)\n",
    "    testset = MyDataset(f\"data/test/{dataset_folder}\", transform)\n",
    "\n",
    "    # trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True, num_workers=8)\n",
    "\n",
    "    # testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n",
    "    testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True, num_workers=8)\n",
    "\n",
    "    exp_name = f\"{dataset_folder}_benchmarking\"\n",
    "\n",
    "    for model_name in model_names:\n",
    "        print(f\"Log: {model_name}\")\n",
    "        model = timm.create_model(model_name, num_classes=2, pretrained=True)\n",
    "        worker = Worker(exp_name, model_name, model, trainloader, testloader, epochs=1, lr=1e-3)\n",
    "        worker.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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